The present invention relates generally to signal processing and specifically to a method for processing a signal for detecting voice activity.
Voice activity detection (VAD) techniques have been widely used in digital voice communications to decide when to enable reduction of a voice data rate to achieve either spectral-efficient voice transmission or power-efficient voice transmission. Such savings are particularly beneficial for wireless and other devices where spectrum and power limitations are an important factor. An essential part of VAD algorithms is to effectively distinguish a voice signal from a background noise signal, where multiple aspects of signal characteristics such as energy level, spectral contents, periodicity, stationary, and the like have to be explored.
Traditional VAD algorithms tend to use heuristic approaches to apply a limited subset of the characteristics to detect voice presence. In practice, it is difficult to achieve a high voice detection rate and low false detection rate due to the heuristic nature of these techniques.
To address the performance issue of heuristic algorithms, more sophisticated algorithms have been developed to simultaneously monitor multiple signal characteristics and try to make a detection decision based on joint metrics. These algorithms demonstrate good performance, but often lead to complicated implementations or, inevitably, become an integrated component of a specific voice encoder algorithm.
Lately, a statistical model based VAD algorithm has been studied and yields good performance and a simple mathematical framework. This algorithm is described in detail in “A Statistical Model-Based Voice Activity Detection”, Jongseo Sohn, Nam Soo Kim, and Wonyong Sung, IEEE Signal Processing Letters, Vol. 6, No. 1, January 1999. The challenge, however, lies in applying this new algorithm to effectively distinguish voice and noise signals, as assumptions or prior knowledge of the SNR is required.
Accordingly, it is an object of the present invention to obviate or mitigate at least some of the abovementioned disadvantages.